People have looked for experts since before the advent of computers. With advances in information retrieval technology and the large-scale availability of digital traces of knowledge-related activities, computer systems that can fully automate the process of locating expertise have become a reality. The past decade has witnessed tremendous interest, and a wealth of results, in expertise retrieval as an emerging subdiscipline in information retrieval. This survey highlights advances in models and algorithms relevant to this field. We draw connections among methods proposed in the literature and summarize them in five groups of basic approaches. These serve as the building blocks for more advanced models that arise when we consider a range of content-based factors that may impact the strength of association between a topic and a person. We also discuss practical aspects of building an expert search system and present applications of the technology in other domains, such as blog distillation and entity retrieval. The limitations of current approaches are also pointed out. We end our survey with a set of conjectures on what the future may hold for expertise retrieval research.

自计算机问世以来，人们一直在寻找专家。随着信息检索技术的进步和与知识有关的活动的数字化痕迹的大规模可用，能够完全自动化寻找专家的过程的计算机系统已成为现实。在过去的十年中，作为一种新兴的信息检索子学科，人们对专业知识检索产生了浓厚的兴趣并取得了丰硕的成果。该调查重点介绍了与此领域相关的模型和算法方面的进步。我们在文献中提出的方法之间建立联系，并将其归纳为五组基本方法。当我们考虑可能影响主题与人之间的关联强度的一系列基于内容的因素时，这些便是构建更高级模型的基础。我们还将讨论构建专家搜索系统的实际方面，并介绍该技术在其他领域的应用，例如博客蒸馏和实体检索。还指出了当前方法的局限性。我们以一系列关于专家检索研究的未来前景的推测来结束调查。